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This paper analyzes the relationship between fiscal and structural policies and gender inequality in education and labor force participation for countries at different stages of development. Due to the substantial of possible factors that link with gender inequality ly highlighted in the literature, we pay particular attention to addressing model uncertainty and using various statistical methods to find the variables with the strongest links to gender gaps.
We find that higher public spending on education, better sanitation facilities, low adolescent fertility, and narrower marriage age gaps are ificantly related to narrower gender gaps in education. We also find that better infrastructure, a stronger institutional environment, more equal legal rights, and low adolescent fertility rates are strongly associated with higher female labor force participation.
When labor market protection is low, an increase in protection is associated with a narrowing of labor force participation gaps between men and women. But when labor market protection levels are high, an increase in protection is associated with a widening in labor force participation gaps. Despite progress of the recent decades, gender gaps in various areas of economic opportunities and outcomes Women seeking men Yangyang mb, with ificant macroeconomic consequences. Indeed, vast empirical evidence exists to show that ificant macroeconomic gains can be realized when women are able to develop their full labor market potential Elborgh-Woytek and othersCuberes and Teignier ; Kochhar, Jain-Chandra and Newiak, What can policymakers do, then, to promote gender equality in economic empowerment?
Building on evidence from numerous microeconomic and macroeconomic studies, this paper conducts an empirical analysis based on macroeconomic data to estimate the impact of fiscal and structural policies on gender inequality in a sample of countries from to Our study focuses in particular on policies that could address gender gaps in labor force participation and educational opportunities for women, as measured by years of schooling and years in tertiary education.
Realizing that there is no silver-bullet for policies to take effect across all income levels, we also examine whether certain policies are more critical in emerging markets and low-income countries LICs than in advanced economies. The novelty of our paper is that it applies Bayesian Model Averaging BMA to identify the most fundamental and robust factors that link to gender inequality.
Against the background of the large of potential determinants of gender inequality suggested in the literature, this methodology helps address model uncertainty as part of the statistical methodology Fernandez and others ; Sala-i-Martin ; Paorgiou and Masanjala BMA is well established in the growth literature to address the complications posed by the fact that a vast of determinants have been proposed to explain real GDP per capita growth for instance, Durlauf and others surveyed determinants of economic growth.
Bayesian model averaging has also been used to introduce new development determinants into the literature Eicher and Newiak While the of suggested drivers of gender equality in the literature is large, no other paper has yet addressed model uncertainty to the best of our knowledge. The of our paper should be read as robust associations between gender equality and certain factors, rather than necessarily as causal relationships. The paper is organized as follows.
Section II reviews the existing literature; Section III presents stylized facts on the relationship between macroeconomic and structural policies, and gender gaps in advanced economies, emerging markets and LICs. Section VI discusses the conclusions and policy implications. Gender equality encompasses a variety of dimensions, such as equality in the access to education, health and financial access for women and men, equality in labor force participation, and political representation.
When analyzing the impact of policies on gender equality, the cross-country literature often focuses on labor market outcomes, namely, female labor force participation or employment, while studies at the country level have also more deeply analyzed how policies impact inequality of opportunity, such as school enrollment rates.
For each outcome variable, papers have explored many potential determinants of gender gaps. Micro-level studies—both theoretical and empirical—are particularly insightful in this respect, and provide the underpinnings to the analysis of this paper. The main areas of work in the theoretical and empirical literature—both at the micro and macro level and covering cross-country and panel-studies—are summarized below. Since this literature is vast, this overview does not aim to be exhaustive. Our paper presents a systematic approach to looking into the ificance of factors ly identified in the literature.
A of studies have pointed to the theoretical underpinning of female labor supply. Female labor supply is often modeled using the framework of the time allocation model Beckerwhich posits that women make their labor supply decisions not only considering leisure and labor, but also home-based production of goods and services including caring for children. Working for a wage is chosen only if earnings at least make up for the lost home production and the associated costsimplying a higher elasticity of female labor supply to wages. Many studies have emphasized the importance of education in models of female labor supply.
Using micro data, Eckstein and Lifshitz estimate a dynamic stochastic female labor supply model with discrete choice contained in Eckstein and Wolpin and find that changes in education and wages play a large role in explaining female employment, with the former ing for a third of the increase in female employment, and the latter explaining about 20 percent. Fernandez and Wong develop a dynamic life-cycle model with incomplete markets and risk-averse agents who differ in their educational endowments and make work, consumption, and savings decisions. Empirical work has shown that fertility and higher marriage rates ificantly affect female labor force participation.
Mishra and Smyth estimate that a 1 Women seeking men Yangyang mb increase in the fertility rate in a 0. While there is a negative relationship between the variables at the individual country level, there is a positive relationship between fertility and female labor force participation at the cross-country level.
They find that women living in countries where men participate more in-home production are better able to combine motherhood with work outside the house, leading to greater participation in the labor force at relatively high fertility levels. The trade-off between family and work is also reflected in a negative correlation between female labor force participation and marriage rates.
Luo exploits exogenous variation in the US marriage market caused by World War II casualties to show that marriage is an important opportunity cost that hinders women from becoming entrepreneurs. On the revenue side, tax credits or benefits for low-wage earners can stimulate labor force participation, including among women. By reducing the net tax liability or even turning it negative, tax credits increase Women seeking men Yangyang mb net income gain from accepting a job.
Such credits are usually phased out as income rises. Policies can also build on the fact that female labor supply is more responsive to taxes than male labor supply. For example, a switch from family income taxation to individual income taxation that reduces the tax burden for predominantly female secondary earners can support female labor force participation, while it would affect the less-tax-elastic male labor supply to a smaller extent Elborgh-Woytek and others As for expenditure policy, higher spending on infrastructure and education, as well as better access to comprehensive, affordable, and high-quality child care supports female employment Gong, Breunig, and King The elasticity of female labor supply with respect to the price of child care has been estimated to range from —0.
Thus, reducing the price of childcare by 50 percent could be associated with an increase of 6. Other studies document the importance of public infrastructure to boost the participation of women in the labor force.
Norando finds that a large part of the difference in female labor force participation rates in between the United States, on the one hand, and Brazil and Mexico, on the other, can be explained by the availability of basic infrastructure electricity and running water. Using micro data, Das et al. Gonzales and others Women seeking men Yangyang mb the effect of gender-based legal restrictions and other policy choices and demographic characteristics on female labor force participation. Female labor force participation is positively correlated with educational attainment for women. Calibrating a dynamic model of labor supply, Eckstein and Lifshitz find that one-third of the increase in female employment during the last century in the United States can be attributed to education.
In an empirical exercise, Steinberg and Nakaneshow that a one standard deviation increase in the education level in Organization for Economic Cooperation and Development OECD countries is associated with a 3 percentage points increase in female labor force participation. A lower female age at marriage and large age gaps between men and women at marriage have been associated with high gender inequality. For instance, using micro data from rural Bangladesh, Field and Ambrus show that each additional year that marriage is delayed is associated with about one- fifth additional year of schooling and 5.
Arguing that a lower age of marriage for women may simply reflect lower marriage ages for both men and women in a society, Stimple and Stadelmann test the relationship between the gap between men and women at marriage and estimate that an additional age difference between husband and wife of one year reduces female secondary schooling completion rates by 14 percentage points.
This, in turn, adversely impacts female education more than male education, therefore increasing the education gap. In this paper, we construct a large country-year panel dataset to examine the determinants of gender gaps in a systematic way.
As noted in the literature survey above, many variables have been proposed in both macroeconomic and microeconomic studies as potential determinants of gender gaps. The large of potential determinants creates complications in several aspects.
Before turning to the empirical investigation, we take a preliminary look at the data on our sample of countries from toto uncover possible correlations among policy variables and gender inequality in education and labor force participation, as well as highlight any differences between developing countries, emerging markets and advanced countries.
However, disaggregating advanced economies from emerging and developing countries shows that the size of gender gaps varies among different country groups e. Stylized facts point to a of possible determinants of gender gaps in education :. Similarly, we find evidence for a range of possible determinants of gender gaps in labor force participation gaps :. We estimate a fixed-effect panel regression to assess the impact of policies on gender gaps, while controlling for country structural characteristics.
Specifically, we estimate the following relationship. The model is estimated initially with ordinary least squares, and then with the Bayesian Model Averaging BMA to check for robustness. Note that the specification does not allow us to make statements about causality; the should rather be read as associations. Several factors are associated with the gender gap in labor force participation, including at the policy level Table 2. The on labor market protection raise the question on whether the effect arises through higher female labor force participation or possibly lower male labor force participation.
To examine these possible asymmetries for men and women, we report also the of separate regressions of male and female labor force participation rates on all the determinants. We find that stronger labor protection laws ificantly increase female labor force participation rate. There is some evidence, albeit weak, that stronger labor protection laws lower male labor force participation rate Table 3column 1 and 2. These findings suggest that the Women seeking men Yangyang mb labor protection narrows the gender gap in the labor market because better protection encourages females to participate in the labor market, whereas male workers do not benefit much from better protection.
We find similar, and even stronger, effects if we regress employment Women seeking men Yangyang mb population ratio on our set of explanatory variables Table 3column 3 and 4. We also investigate the effects of each sub-index of labor market protection on female labor force participation. We find that among the 8 sub-indices from the World Bank Doing Business Index, maximum of working days per week and notice period for redundancy dismissal both have a ificant impact on female labor force participation Table A2 in Annex II : A larger maximum of working days is associated with a wider gender gap in labor force participation, whereas a longer notice period for dismissal narrows the gender gap in the labor market.
These from the sub-indices are consistent with our finding from the overall index that stronger protection encourages women to participate in the labor market. A wide range of factors that link to gender equality has been tested in the literature and is confirmed in this study. However, it is still unclear to policymakers which factors are the most fundamental and robust. Mis-specified econometric models lead to biased estimates, and classical statistical approaches offers little help with model uncertainty, especially when the sample is small.
Large panels, like the one we are using—covering a vast of countries over the past three decades—alleviate the small sample issue.
For policy recommendations, however, it is important to test the robustness of the determinants of gender inequality. We use Bayesian Model Averaging BMA to address model uncertainty and examine the robustness of each potential determinant. BMA is a statistical technique which offers a way to think about model uncertainty Leamer ; Raftery el al. This requires departing from the classical statistical framework and adopt Bayesian updating. More specifically, BMA averages across a large set of models for a given set of priors. The weights also depend on the choice of priors specified.
Following this classification, we use BMA to select the most robust determinants of gender inequality, while the level of development and its square is set to be included in all regressions as a basic control. The highlight a smaller set of variables that is robustly related with lower education gaps compared to the frequentist approach.
A substantial of country characteristics and policies are robustly related to gender gaps in labor force participation. This section uses the above empirical to quantify the main policy and structural constraints to female labor force participation across regions in — In particular, we use ly derived coefficients obtained from the BMA analysis and regional averages over a five-year period to decompose the male-to-female labor force participation gap into the impact from:.
The highlight that policies have contributed to a lower labor force participation gap in all regions, but to a different extent. Figure 3 plots the determinants of the labor force participation gap for all regions, highlighting that:. The large impact of policies begs the question which policies have yielded the biggest bang for the buck in each region Figure 4.
The novel contribution of this paper is the identification of the most important and robust determinants of gender inequality, thus providing a more useful guide for policy action. We complement the existing literature by explicitly addressing model uncertainty that is inherent in all empirical estimations where the proposed set of potential determinants is large. In fact, in addition to standard fixed-effects estimations for a large Women seeking men Yangyang mb, our paper is the first to apply Bayesian Model Averaging—a methodology that is specifically deed to highlight factors that are robustly related to a variable of interest—to the literature on gender gaps in education and labor force participation.
With that, our paper is able to highlight policy areas that are likely to yield the biggest bang for the buck. What types of economic policies can then boost gender equality? First of all, educating girls has paid off in all regions, with a large potential of increasing female education further in sub-Saharan Africa and South Asia.
Fiscal policies aiming at improving infrastructure, especially sanitation facilities in low-income countries also matter. Labor market policies can also support gender equality. However, our suggest that strengthening job protection can help narrow the labor force participation gaps only in institutional settings where job protection is low.
Topics Business and Economics. Banks and Banking. Corporate Finance. Corporate Governance. Corporate Taxation. Economic Development. Economic Theory. Economics: General. Environmental Economics. Exports and Imports. Finance: General. Financial Risk Management. Foreign Exchange. Industries: Automobile.Women seeking men Yangyang mb
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